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20202024
most citedIncreasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks

8 citations · 27 across the 10 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.CV2020

CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images

Linchen Qian, Jiasong Chen, Timur Urakov +2

Ambiguity is inevitable in medical images, which often results in different image interpretations (e.g. object boundaries or segmentation maps) from different human experts. Thus,…

cs.CV2020★ 3 cited

An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder

Liang Liang, Linhai Ma, Linchen Qian +1

Deep neural networks (DNNs), especially convolutional neural networks, have achieved superior performance on image classification tasks. However, such performance is only guarantee…

eess.SP2020★ 1 cited

Enhance CNN Robustness Against Noises for Classification of 12-Lead ECG with Variable Length

Linhai Ma, Liang Liang

Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many rese…

cs.CV2020★ 8 cited

Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks

Linhai Ma, Liang Liang

Deep neural networks (DNNs) are vulnerable to adversarial noises. Adversarial training is a general and effective strategy to improve DNN robustness (i.e., accuracy on noisy data)…

eess.SP2020★ 4 cited

Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective

Linhai Ma, Liang Liang

Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the cardiovascular system. Deep neural networks (DNNs), have been developed in many rese…